Sep 12, 2019 · In this paper, we introduce a data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via ...
Coarse-scale PDEs from fine-scale observations via machine learning
www.ncbi.nlm.nih.gov › PMC7043837
In this paper, we introduce a data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via machine-learning ...
Jan 24, 2020 · In this paper, we introduce and implement a framework for systematically extracting coarse-scale observables from microscopic/fine-scale data ...
A data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via machine-learning algorithms using Gaussian ...
Jan 24, 2020 · Lee et al. propose a framework for extracting coarse-scale partial differential equations (PDEs) from fine-scale data using machine learning.
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice ...
Workflow for uncovering coarse-scale PDEs. First, we compute macroscopic variables u and v from the Lattice Boltzmann simulation data (see equation (18) and ...
This paper presents a computational technique to learn the fine-scale dynamics from such coarsely observed data. The method employs inner recurrence of a DNN to ...
The aim of this work is to learn coarse grained PDEs as well as reduced order models of coarse-scale PDEs for multiphase flows using a data-driven approach. In ...
In this paper, we introduce a data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via machine learning ...
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